





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowerCAmelCase_        =							TypeVar('''T''')



class    __lowerCAmelCase			(     Generic[T]	):






			def __init__(self   ,				__magic_name__   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							snake_case_		:       Any | T           =    None

							snake_case_		:       int           =    len(__magic_name__       )
							snake_case_		:       list[T]           =    [any_type for _ in range(self.N       )] + arr
							snake_case_		:       Optional[int]           =    fnc
							self.build()






			def      lowerCamelCase    (self       )	->  None:


							'''simple docstring'''



							for p in range(self.N - 1   ,				0   ,				-1       ):
											snake_case_		:       Optional[Any]           =    self.fn(self.st[p * 2]   ,				self.st[p * 2 + 1]       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  None:


							'''simple docstring'''



							p += self.N
							snake_case_		:       Dict           =    v
							while p > 1:
											snake_case_		:       List[str]           =    p // 2
											snake_case_		:       int           =    self.fn(self.st[p * 2]   ,				self.st[p * 2 + 1]       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  T | None:  # noqa: E741


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       int           =    l + self.N, r + self.N

							snake_case_		:       T | None           =    None
							while l <= r:
											if l % 2 == 1:
															snake_case_		:       Optional[Any]           =    self.st[l] if res is None else self.fn(__magic_name__   ,				self.st[l]       )
											if r % 2 == 0:
															snake_case_		:       Optional[int]           =    self.st[r] if res is None else self.fn(__magic_name__   ,				self.st[r]       )
											snake_case_     ,				snake_case_		:       Dict           =    (l + 1) // 2, (r - 1) // 2
							return res


if __name__ == "__main__":
	from functools import reduce

	lowerCAmelCase_        =							[1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

	lowerCAmelCase_        =							{
	    0: 7,
	    1: 2,
	    2: 6,
	    3: -1_4,
	    4: 5,
	    5: 4,
	    6: 7,
	    7: -1_0,
	    8: 9,
	    9: 1_0,
	    1_0: 1_2,
	    1_1: 1,
	}

	lowerCAmelCase_        =							SegmentTree(test_array, min)
	lowerCAmelCase_        =							SegmentTree(test_array, max)
	lowerCAmelCase_        =							SegmentTree(test_array, lambda a, b: a + b)






	def 	lowerCamelCase_		(							)						->		None:






					"""simple docstring"""


					for i in range(len(_UpperCamelCase					)					):
									for j in range(_UpperCamelCase					,							len(_UpperCamelCase					)					):
													snake_case_		:       Dict           =    reduce(_UpperCamelCase					,							test_array[i : j + 1]					)
													snake_case_		:       Tuple           =    reduce(_UpperCamelCase					,							test_array[i : j + 1]					)
													snake_case_		:       Tuple           =    reduce(lambda _UpperCamelCase					,							_UpperCamelCase					: a + b					,							test_array[i : j + 1]					)
													assert min_range == min_segment_tree.query(_UpperCamelCase					,							_UpperCamelCase					)
													assert max_range == max_segment_tree.query(_UpperCamelCase					,							_UpperCamelCase					)
													assert sum_range == sum_segment_tree.query(_UpperCamelCase					,							_UpperCamelCase					)

	test_all_segments()

	for index, value in test_updates.items():
		lowerCAmelCase_        =							value
		min_segment_tree.update(index, value)
		max_segment_tree.update(index, value)
		sum_segment_tree.update(index, value)
		test_all_segments()








'''simple docstring'''





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       []
			for part_id in partition_order:
						UpperCAmelCase_      :       Any          =       df.where(F"SPARK_PARTITION_ID() = {part_id}"		).collect()
						for row_idx, row in enumerate(A		):
									expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())		)
			return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       Any          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       List[Any]          =       Spark(A		)
			# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
			# that each partition can hold 2 rows.
			spark_builder._repartition_df_if_needed(max_shard_size=1_6		)
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       List[str]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Optional[Any]          =       spark.range(1_0		).repartition(2		)
			UpperCAmelCase_      :       int          =       [1, 0]
			UpperCAmelCase_      :       str          =       _generate_iterable_examples(A			,       A		)  # Reverse the partitions.
			UpperCAmelCase_      :       Optional[Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       A		)

			for i, (row_id, row_dict) in enumerate(generate_fn()		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       expected_row_ids_and_row_dicts[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       str          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[Any]          =       spark.range(1_0		).repartition(1		)
			UpperCAmelCase_      :       Optional[int]          =       SparkExamplesIterable(A		)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(A		):
						assert row_id == F"0_{i}"
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Dict          =       spark.range(3_0		).repartition(3		)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''		) as generator_mock:
						UpperCAmelCase_      :       Any          =       lambda A		: x.reverse()
						UpperCAmelCase_      :       Dict          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [2, 1, 0]		)

						UpperCAmelCase_      :       List[Any]          =       SparkExamplesIterable(A		).shuffle_data_sources(A		)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(A		):
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts[i]
									assert row_id == expected_row_id
									assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       Tuple          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       int          =       spark.range(2_0		).repartition(4		)

			# Partitions 0 and 2
			UpperCAmelCase_      :       int          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=0			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       Union[str, Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [0, 2]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict

			# Partitions 1 and 3
			UpperCAmelCase_      :       Tuple          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=1			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       List[str]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [1, 3]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       int          =       Spark(A		)
			# Choose a small max_shard_size for maximum partitioning.
			spark_builder._repartition_df_if_needed(max_shard_size=1		)
			# The new number of partitions should not be greater than the number of rows.
			assert spark_builder.df.rdd.getNumPartitions() == 1_0_0


